Methods of prognosis of prostate cancer

ABSTRACT

The present invention applies classical survival analysis to genome-wide gene expression profiles of prostate cancers and preoperative prostate-specific antigen levels from prostate cancer patient, to identify prognostic markers of disease relapse that provide additional predictive value relative to prostate-specific antigen concentration. The present invention provides a method of determining prognosis of prostate cancer and predicting prostate cancer outcome of a patient. The method comprises the steps of first establishing the threshold value of at least one prognostic gene of prostate cancer. Then, the amount of the prognostic gene from a prostate tissue of a prostate cancer patient is determined. The amount of the prognostic gene present in that patient is compared with the established threshold value of the prognostic gene, whereby the prognostic outcome of the patient is determined.

This application claims the benefit of provisional application,60/391,309, filed Jun. 24, 2002, which is incorporated herein in itsentirety.

FIELD OF THE INVENTION

The invention relates to the identification of nucleic acid and proteinexpression profiles and nucleic acids, products, and antibodies theretothat are outcome prognostic in prostate cancer.

BACKGROUND OF THE INVENTION

Prostate cancer will account for an estimated 30% (189,000) of newcancer cases in men in the United States in 2002 (1). Many of thesenewly diagnosed cases are a result of the extensive use ofprostate-specific antigen (PSA) screening and the subsequent diagnosisof prostate cancer at an early stage and age. However, despite theintroduction of PSA screening the mortality from prostate cancer hasremained relatively constant. The implications of this are that: (1)there are a large group of men diagnosed with prostate cancer for whomradical treatment is probably unnecessary and who will die with theirprostate cancer rather than from it; and (2) there are a group of menfor whom early detection offers the possibility of cure that may bedenied by delay. Consequently, identifying these groups of men at thetime of diagnosis is critical to the optimal management of prostatecancer.

While the benefits of PSA screening are widely debated, this serummarker remains one of only a small number of preoperative parameters ofprognostic utility. In order to enhance the predictive value ofindividual parameters with outcomes, nomograms have been developed thatincorporate parameters that are measured routinely in clinical practiceto predict the probability of PSA relapse free survival of individualpatients both prior to and following therapy (2-6). Models such as thesecurrently form the basis of routine clinical decision-making, but suchclassification systems cannot explore differences in outcomes observedbetween cancers with similar histopathological features. Hence, thereremains a critical need for increased accuracy in the subcategorizationof prostate cancers to identify those with an aggressive phenotype.

There are a considerable number of publications assessing the ability ofbiomarkers to predict an earlier time to relapse of prostate cancerfollowing radical prostatectomy (reviewed in ref. (17)). Despite thesedata, there remain no molecular markers of routine clinical utilitywhich differentiate localized prostate cancers with an aggressivephenotype, and clinicians still rely on conventional preoperative andpostoperative prognostic indicators such as pretreatment PSA levels,pathological stage and Gleason grade in routine decision-making. Thismost likely reflects the fact that studies that have correlateddifferences in gene expression with patient outcome have assessedcandidate genes with limited predictive power that provide no additionalprognostic information above the conventional variables. Thisaccentuates the need to discover novel genes with strong predictiveability.

One approach is to define patterns of gene expression that correlatewith disease phenotype and patient outcome. Here, we undertook asystematic search for novel biomarkers of prostate cancer prognosis byoutcome-based analyses of transcript profiles.

SUMMARY OF INVENTION

The present invention evaluates gene expression profile and identifiesprognostic genes of prostate cancers. The present invention provides amethod of determining prognosis of prostate cancer and predictingprostate cancer outcome of a patient. The method comprises the steps offirst establishing the threshold value of at least one prognostic geneof prostate cancer. Then, the amount of the prognostic gene from aprostate tissue of a patient inflicted of prostate cancer is determined.The amount of the prognostic gene present in that patient is comparedwith the established threshold value of the prognostic gene, whereby theprognostic outcome of the patient is determined.

In certain embodiments, the amount of the prognostic gene is determinedby the quantitation of a transcript encoding the sequence of theprognostic gene; or a polypeptide encoded by the transcript. Thequantitation of the transcript can be based on hybridization to thetranscript. The quantitation of the polypeptide can be based on antibodydetection. The method optionally comprises a step of amplifying nucleicacids from the tissue sample before the evaluating. In some embodiments,the evaluating is of a plurality of sequences. The method may furthercomprises determining prostate-specific antigen (PSA) level. Theprognosis contributes to selection of a therapeutic strategy.

BRIEF DESCRIPTION OF THE FIGURES

The file of this patent contains at least one drawing executed in color.Copies of this patent with color drawing(s) will be provided by thePatent and Trademark Office upon request and payment of the necessaryfee.

FIG. 1 shows cluster analysis of prostate cancer samples from 72patients treated for localised prostate cancer by RP. Each columnrepresents a single RP specimen and each row represents one of the 264genes which demonstrated a strong association with PSA relapse in ourmodel. The dendogram at the top shows the degree to which each prostatecancer is related to the others with respect to gene expression. The 17patients known to have experienced a PSA relapse are indicated by an“R”. The relative level of expression is indicated by the color scale atthe bottom and is indicative of the normalized average intensity unitsof fluorescence signal detected by microarray analysis.

FIG. 2A shows the expression of trp-p8 mRNA detected by oligonucleotidemicroarray in prostate cancer samples and in normal body tissues.Samples are: prostate cancer 1-74, adrenal glands 75-77, aorta 78-80,artery 81-83, bladder 84-86, bone marrow 87-89, colonic epithelium90-92, cerebral cortex, 93-95, colon 96-98, colonic muscle 99-101,esophagus 102-104, heart 105-107, kidney 108-110, liver 111-113, lung114-116, lymph node 117-119, muscle 120-122, oral mucosa 123-125,pharyngeal mucosa 126-128, pancreas 129-131, parathyroid glands 132-133,pituitary 134-136, prostate 137-143, retina 144-146, skin 147-149, smallintestine 150-152, spleen 153-155, stomach 156-158, trachea 159-161,tongue 162-164, ureter 165-167, vagus nerve 168-170, vein 171-174.

FIG. 2B shows the expression of trp-p8 mRNA, and FIG. 2C shows the PSAmRNA; both detected by oligonucleotide microarray in LuCaP-35 tumors atdays 0 to 100 post castration. The expression level of trp-p8 and PSA isshown as normalized average intensity units (Y-axis) of fluorescencesignal detected by microarray analysis.

units (Y-axis) of fluorescence signal detected by microarray analysis.

FIG. 3 shows the Trp-p8 mRNA expression detected by in situhybridization in radical prostatectomy cases treated with or withoutneoadjuvant hormone therapy prior to surgery. FIG. 3A: A prostate cancerfrom a patient treated with RP only showing positive trp-p8 mRNAexpression in malignant prostate epithelium. FIG. 3B: A prostate cancerfrom a patient treated with RP and NHT showing positive trp-p8 mRNAexpression. FIG. 3C: A prostate cancer from a patient treated with RPonly with no detectable trp-p8 mRNA expression in the malignantepithelium, and Figure D: A prostate cancer from a patient treated withpreoperative NHT with no evidence of trp-p8 expression.

FIG. 4A shows the trp-p8 protein sequence. FIG. 4B shows the trp-p8 mRNAsequence.

DETAILED DESCRIPTION OF INVENTION

Current models of prostate cancer classification are poor atdistinguishing between tumors that have similar histopathologicalfeatures but vary in clinical course and outcome. In the presentinvention, we have applied classical survival analysis to genome-widegene expression profiles of prostate cancers and preoperativeprostate-specific antigen levels from each patient, to identifyprognostic markers of disease relapse that provide additional predictivevalue relative to prostate-specific antigen concentration. The presentinvention demonstrates that multivariable survival analysis can beapplied to gene expression profiles of prostate cancers with censoredfollow-up data and used to identify molecular markers of prostate cancerrelapse with strong predictive power and relevance to the etiology ofthis disease.

Prostate Cancer and Treatments

Prostate cancer is found mainly in older men. Prostate cancer is themost commonly diagnosed internal malignancy and second most common causeof cancer death in men in the U.S., resulting in approximately 40,000deaths each year. Landis et al. (1998) CA Cancer J. Clin. 48:6-29; andGreenlee, et al. (2000) CA Cancer J. Clin. 50:7-13. The incidence ofprostate cancer has been increasing rapidly over the past 20 years inmany parts of the world. Nakata, et al. (2000) Int. J. Urol 7:254-257;and Majeed, et al. (2000) BJU Int. 85:1058-1062. It develops as theresult of a pathologic transformation of normal prostate cells. Intumorigenesis, the cancer cell undergoes initiation, proliferation, andloss of contact inhibition, culminating in invasion of surroundingtissue and, ultimately, metastasis.

Prostate cancer is a disease in which malignant (cancer) cells form inthe tissues of the prostate. The prostate is a gland in the malereproductive system located just below the bladder (the organ thatcollects and empties urine) and in front of the rectum (the lower partof the intestine). It is about the size of a walnut and surrounds partof the urethra (the tube that empties urine from the bladder). Theprostate gland produces fluid that makes up part of the semen. Seegenerally, Boyle, et al. (2002) Textbook of Prostate Cancer Isis MedicalMedia, ISBN: 1901865304; Kantoff (ed. 2002) Prostate Cancer: Principlesand Practice Lippincott, ISBN: 0781720060; Carroll (2001) ProstateCancer Decker, ISBN: 1550091301; Belldegrun, et al. (2000) NewPerspectives in Prostate Cancer Isis Medical Media, ISBN: 1901865568;Lepor (1999) Prostatic Diseases Saunders, ISBN: 072167416X; Petrovich,et al. (eds. 1996) Carcinoma of the Prostate: Innovations in Management,Springer Verlag, ISBN: 3540587497; and standard prostate cancer medicaltexts.

Four types of standard treatment are used for prostate cancer: watchfulwaiting, surgery, radiation therapy, or hormone ablation therapy. See,e.g., the National Cancer Institute (NCI) description of prostatecancer, www.cancer.gov.

Watchful waiting is closely monitoring a patient's condition butwithholding treatment until symptoms appear or change. This is usuallyused in older men with other medical problems and early stage disease.

Surgery is usually offered to prostate cancer patients in good healthwho are younger than 70 years old. Main surgery options are pelviclymphadenectomy, radical protatectomy, perineal prostatectomy, andtransurethral resection of the prostate.

Pelvic lymphadenectomy is a surgical procedure to take out lymph nodesin the pelvis to see if they contain cancer. If the lymph nodes containcancer, the doctor will not remove the prostate and may recommend othertreatment. Radical prostatectomy (RP) is surgery to remove the entireprostate. Radical prostatectomy is done only if tests show the cancerhas not spread outside the prostate. The two types of radicalprostatectomy are retropubic prostatectomy, which removes the prostatethrough an incision made in the abdominal wall, and removal ofsurrounding lymph nodes (lymphadenectomy) can be done at the same time;and perineal prostatectomy, which is surgery to remove the prostatethrough an incision made between the scrotum and the anus, and ifsurrounding lymph nodes are to be removed, this is usually done througha separate incision. Transurethral resection of the prostate is asurgical procedure to remove tissue from the prostate using aninstrument inserted through the urethra. This operation is sometimesdone to relieve symptoms caused by the tumor before other treatment isgiven. Transurethral resection of the prostate may also be done in menwho cannot have a radical prostatectomy because of age or illness.

Impotence and leakage of urine from the bladder or stool from the rectummay occur in men treated with surgery. In some cases, doctors can use atechnique known as nerve-sparing surgery. This type of surgery may savethe nerves that control erection. However, men with large tumors ortumors that are very close to the nerves may not be able to have thissurgery.

Radiation therapy is the use of x-rays or other types of radiation tokill cancer cells and shrink tumors. Radiation therapy may use externalradiation (using a machine outside the body) or internal radiation.Internal radiation involves putting radioisotopes (materials thatproduce radiation) through thin plastic tubes into the area where cancercells are found. Prostate cancer is treated with external and internal(implant) radiation. Radiation therapy may be used alone or in additionto surgery. Impotence and urinary problems may occur in men treated withradiation therapy.

Hormone therapy is the fourth of the standard treatments. Hormones arechemicals produced by glands in the body and circulated in thebloodstream. Hormone therapy is the use of hormones to stop cancer cellsfrom growing. Male hormones (especially testosterone) can help prostatecancer grow. To stop the cancer from growing, female hormones or drugsthat decrease production of male hormones may be given. Hormone therapyused in the treatment of prostate cancer may include the following:estrogens (hormones that promote female sex characteristics) can preventthe testicles from producing testosterone, however, estrogens are seldomused today in the treatment of prostate cancer because of the risk ofserious side effects; luteinizing hormone-releasing hormone agonistsalso can prevent the testicles from producing testosterone, e.g.,leuprolide, goserelin, and buserelin; antiandrogens can block the actionof androgens (hormones that promote male sex characteristics), twoexamples are flutamide and bicalutamide; drugs that can prevent theadrenal glands from making androgens include ketoconazole andaminoglutethimide; and orchiectomy is surgery to remove the testicles,the main source of male hormones, to decrease hormone production. Hotflashes, impaired sexual function, and loss of desire for sex may occurin men treated with hormone therapy.

Deaths from prostate cancer are typically a result of metastasis of aprostate tumor. Therefore, early detection of the development ofprostate cancer is critical in reducing mortality from this disease.Measuring levels of prostate-specific antigen (PSA) has become a verycommon method for early detection and screening, and may havecontributed to the slight decrease in the mortality rate from prostatecancer in recent years. Nowroozi, et al. (1998) Cancer Control5:522-531. However, many cases are not diagnosed until the disease hasprogressed to an advanced stage.

Prognosis, Outcome

Prognosis is typically recognized as a forecast of the probable courseand outcome of a disease. See Dorland's Medical Dictionary. As such, itinvolves inputs of both statistical probability, requiring numbers ofsamples, and outcome data. Herein, outcome data is utilized in the formof prostate cancer recurrence after RP. A patient population of manydozens is included, providing statistical power.

The ability to determine which cases of prostate cancer will respond totreatment, and to which type of treatment, would be useful inappropriate allocation of treatment resources. As indicated above, thevarious standard therapies have significantly different risks andpotential side effects. Accurate prognosis would also minimizeapplication of treatment regimens which have low likelihood of success.Such also could avoid delay of the application of alternative treatmentswhich may have higher likelihoods of success for a particular presentedcase. Thus, the ability to evaluate individual prostate cases formarkers which subset into responsive and non-responsive groups forparticular treatments are very useful.

Current models of prostate cancer classification are poor atdistinguishing between tumors that have similar histopathologicalfeatures but vary in clinical course and outcome. Kattan, et al. (1998)J. Nat'l Cancer Inst. 90:766-771; and Kattan, et al. (1999) J. Clin.Oncol. 17:1499-1507. Identification of novel prognostic molecularmarkers is a priority if radical treatment is to be offered on a moreselective basis to those prostate cancer patients with clinicallysignificant disease. A novel strategy is described to discover molecularmarkers for prostate cancer prognosis by assessing genome-wide geneexpression in many localized prostate cancers and modeling these databased on each patient's known clinical outcome and preoperative serumprostate-specific antigen concentration. The study herein is directed tomolecularly define different forms of prostate cancer which cantranslate directly into prognosis. And such prognosis allows forapplication of a treatment regimen having a greater statisticallikelihood of cost effective treatments and minimization of negativeside effects from the different treatment options.

Prostate cancer biopsy samples were collected and analyzed for geneexpression across most genes of the human genome. Among genes detectedat appropriate levels, correlations with outcome data were evaluated.Genes whose expression levels correlated with statistical significanceto outcome data were identified.

This approach identified about 270 genes that demonstrated a strongassociation (P<0.01) with disease outcome, e.g., prostate cancerrelapse, and were superior in their predictive ability relative toprostate-specific antigen levels, one of the standard markers. One ofthese genes, the putative calcium channel protein trp-p8, isandrogen-regulated and loss of trp-p8 appears to be associated withaggressive disease. The findings provide a method of survival analysisof gene expression profiles of cancers with censored follow-up data andidentify novel molecular markers of prostate cancer progression withstrong predictive power that may be used to select prostate cancers withan aggressive phenotype.

Thus, the invention herein provides statistical correlations of markerexpression in appropriate samples with disease outcome.

Survival Analysis

The present invention provides the application of classicalmultivariable survival analysis to a prostate cancer microarray data setincorporating the expression profiles of over 46,000 genes, to identifymarkers of disease outcome. This technique provides several significantadvances over previous methods of analyses that have been used todiscover markers of disease outcome from microarray data. In contrast topreviously described statistical methods that rely on the classificationof tumors based on known outcome (18) or known classifiers of patientoutcome (eg. estrogen receptor status) (19, 20), this technique providesfor censored data. This enables these analyses to proceed prior to theoccurrence of all events, in this case, PSA relapse. Moreover, thissurvival analysis incorporates the time taken to PSA relapse and mayalso include covariates (eg. preoperative serum PSA levels) in order toidentify genes that provide additional predictive value aboveconventional markers of outcome. The statistical analyses describedherein have also incorporated a stringent method of estimating the pFDRthat was recently described (10). This method is designed specificallyfor the analysis of microarray data where general dependence betweenhypotheses or “clumpy dependence” exists, where 50 or more genesinteract in common pathways to produce some overall process (10).However, this is the first instance that it has been applied tomicroarray data from a survival analysis.

A recently published analysis to discover new markers of prostate canceroutcome utilized microarray analyses of prostate cancers to classifysmall groups of tumors where the recurrence status was known (21). Thatstudy found that no single gene was statistically associated withrecurrence at P<0.05 and instead adopted a 5-gene model that mostcommonly included chromogranin A and inositol triphosphate receptor 3(IP3R). The significant differences between our study and thesepreviously published data are (1) our adoption of a Cox proportionalhazards model, and (2) our observation that 277 individual genes werepredictive for prostate cancer relapse, none of which overlapped withthe genes in the 5-gene model identified by Singh et al. (2002). Thereare two prevailing explanations for the latter discrepancy. Firstly, thenumber of genes interrogated by oligonucleotide microarrays in our studywas 4-fold greater; trp-p8 is an example of a gene which was not presentin the oligonucleotide array used in the previous study. As a result,the genes identified by Singh et al. (2002), were associated with Pvalues of less significance than those presented in Tables 1 and 2.Secondly, by utilizing a statistical method that applies to censoreddata, we were able to take into account the varying times to prostatecancer relapse in this model. Therefore, we were able to use our fulldata set in the analysis, rather than restricting the analysis to thosepatients with a specified length of follow-up. The larger data set andconcomitant increase in statistical power may also contribute to ourresults differing from those of Singh et al.

The TRP channels are made of subunits with six membrane-spanning domainswith both carboxy and amino termini located intracellularly thatprobably form into tetramers to form non-selective cationic channels,which allow for the influx of calcium ions into the cell. Trp-p8 orTRPM8 is a member of the TRPM subfamily of TRP ion channels that havepotential roles in Ca²⁺-dependent signaling, control of cell cycleproliferation, cell division and cell migration. Ligand binding to somemembrane receptors initiates a sequence of events that lead to theactivation of phospholipase C, generating inositol-1,4,5-triphosphatewhich opens the intracellular ion channel IP3R and liberates Ca²⁺ fromthe endoplasmic reticulum. Activation of the TRP channels accompaniesthis chain of events, allowing the influx of calcium ions into thecells, although their activation is not necessarily directly linked toCa²⁺ depletion from internal stores (22). Calnexin, which is alsoidentified in this analysis as a marker of potential prognostic utility(P=0.004), is believed to be a key chaperone involved in the folding,assembly and oligomerization of newly synthesised IP3R receptors (24).Thus, our study implicates an important role for thephosphatidylinositol signal transduction.

Our observation that loss of trp-p8 is associated with a poor prognosisis also reminiscent of the prognostic role of another of the TRPMsubfamily, TRPM1 or melastatin, in melanoma. Downregulation ofmelastatin mRNA in primary cutaneous melanoma is a prognostic marker formetastasis in patients with localized melanoma and is independent ofconventional clinicopathological predictors of metastases (25). Recentstudies showed that the rat (26) and mouse (27) orthologues of trp-p8are functional calcium channels that respond to cold stimuli. Althoughcold is unlikely to be the natural stimulus for trp-p8 in the prostate,the implication that the human trp-p8 protein may be a functional Ca²⁺channel suggests a role in the regulation of intracellular Ca²⁺ levelswith possible effects on cell motility, cell proliferation andresistance to apoptotic stimuli.

In summary, our analyses have identified a group of genes that stronglycorrelate with prostate cancer relapse and contribute unique informationto relapse prediction above preoperative PSA.

Prognosis Determination

One application of the survival analysis results is to generate aprognostic test for prostate cancer. First, we use TAQMAN® analysis todetermine the absolute levels of prognostic genes in 75-150 or moreprostate cancer patients. Then we correlate the absolute levels of theprognostic genes with patient outcome by a statistical analysis anddetermine threshold levels of prognostic genes; from which we establisha profile of the threshold level of each prognostic gene associated witha good outcome. For determining the prognosis of a prostate cancerpatient, the absolute level of one or more prognostic genes of thispatient is determined. Then the absolute level of one or more prognosticgenes of this patient is compared with the above established thresholdvalues. Absolute level higher (or lower depending on the prognosticgene) than the threshold values indicates good outcome.

The normalized quantitative level of absolute gene expression of aprognostic gene, from which outcome is predicted, is determined first.Quantitative polymerase chain reaction (PCR)-based methods can beapplied. RT-PCR (reverse transcriptase PCR) primers are designed forselected prognostic genes, in order to perform a TaqMang® analysis.

TAQMAN® analysis is a real-time quantitative PCR, which is a powerfulmethod used for gene expression analysis, genotyping, pathogendetection/quantitation, mutation screening and DNA quantitation. See,e.g., Bartlett (2003) PCR Protocols (2^(d) ed.) Humana Press; andO'Connell (2002) RT-PCR Protocols, Humana Press. The technology uses,e.g., an ABI Prism instrument (TAQMAN® ) to detect accumulation of PCRproducts continuously during the PCR process thus allowing easy andaccurate quantitation in the early exponential phase of PCR. The basisfor PCR quantitation in the ABI instrument is to continuously measurePCR product accumulation using a dual-labeled flourogenicoligonucleotide probe called a TAQMAN® probe. This probe is composed ofa short (ca. 20-25 bases) oligodeoxynucleotide that is labeled with twodifferent flourescent dyes. On the 5′ terminus is a reporter dye and onthe 3′ terminus is a quenching dye. This oligonucleotide probe sequenceis homologous to an internal target sequence present in the PCRamplicon. When the probe is intact, energy transfer occurs between thetwo flourophors and emission from the reporter is quenched by thequencher. During the extension phase of PCR, the probe is cleaved by 5′nuclease activity of Taq polymerase thereby releasing the reporter fromthe oligonucleotide-quencher and producing an increase in reporteremission intensity. The laser light source excites each well and a CCDcamera measures the fluorescence spectrum and intensity from each wellto generate real-time data during PCR amplification. The ABI Prismsoftware examines the fluorescence intensity of reporter and quencherdyes and calculates the increase in normalized reporter emissionintensity over the course of the amplification. The results are thenplotted versus time, represented by cycle number, to produce acontinuous measure of PCR amplification. To provide precisequantification of initial target in each PCR reaction, the amplificationplot is examined at a point during the early log phase of productaccumulation. This is accomplished by assigning a fluorescence thresholdabove background and determining the time point at which each sample'samplification plot reaches the threshold (defined as the threshold cyclenumber or CT). Differences in threshold cycle number are used toquantify the relative amount of PCR target contained within each tube asdescribed previously.

The TAQMAN® primers are designed within the open-reading frame of theprognostic gene of interest so that the amplicon averages 80 bp.Prostate tissue samples from 70-150 or more prostate cancer patientswith known histories are collected and RNA is extracted from thesesamples using standard methods. TAQMAN® analysis is performed on thesesamples for the appropriate genes. Using the TAQMAN® analysis, thenormalized absolute levels of the prognostic genes are then correlatedwith patient outcome. Using statistical analyses the threshold level ofgene expression, which predicts outcome, is then determined. Subsequentpatient samples can then be analyzed for potential of relapse and thephysician can better define the patient treatment based on whether thepatient is predicted to relapse. Subsetting of the data into variousoutcomes is achieved through statistical analyses. (Snedecor and Cochran(1994) Statistical Methods (8^(th) ed.) Iowa State University Press; andDuda, et al. (2001) Pattern Classification (2^(d) ed.) Wiley and Sons.)

Genes, Markers, Kits

The present study provides specific identification of multiple geneswhose expression levels in biological samples will serve as markers toevaluate prostate cancer cases. These markers have been selected forstatistical correlation to disease outcome data on a large number ofprostate cancer patients.

The expression levels of these markers in a biological sample may beevaluated by many methods. They may be evaluated for RNA expressionlevels. Hybridization methods are typically used, and may take the formof a PCR or related amplification method. Alternatively, a number ofqualitative or quantitative hybridization methods may be used, typicallywith some standard of comparison, e.g., actin message. Alternatively,measurement of protein levels may performed by many means. Typically,antibody based methods are used, e.g., ELISA, radioimmunoassay, etc.,which may not require isolation of the specific marker from otherproteins. Other means for evaluation of expression levels may be appliedupon purification of the marker. Antibody purification may be performed,though separation of protein from others, and evaluation of specificbands or peaks on protein separation may provide the same results. Thus,e.g., mass spectroscopy of a protein sample may indicate thatquantitation of a particular peak will allow detection of thecorresponding marker. Multidimensional protein separations may providefor quantitation of specific purified entities.

Tables 1A-C describe markers of the invention useful for the prognosisof prostate cancer.

-   Table 1A shows radical prostatectomy samples that were analyzed    using the Eos Hu03 GENECHIP®, which contains 59680 probesets. Each    probeset's intensity measure was entered as a continuous explanatory    variable in a Cox proportional hazards regression survival analysis    predicting relapse. Pretreatment PSA concentration was entered as a    predictor in each analysis. The interquartile range hazard ratio    (IQR HR) for each probeset was then calculated. This approach was    used since in conventional Cox proportional hazards analyses, the    hazards ratios for a covariate are computed by raising e, the base    of natural logarithms, to the power of its regression coefficient.    However, because the expression data are treated here as continuous    covariates, hazards ratios expressed in this manner illustrate only    the change in risk of relapse associated with a change of 1 unit on    the expression scale, a change too small to be meaningful. To put    the hazard ratios and associated confidence limits on a more    interpretable scale, presented here is the hazards ratio associated    with a change in expression values equivalent to 1 interquartile    range (IQR) of the sample data for each probeset. The IQR is simply    the 75th percentile minus the 25th percentile, and thus contains the    middle 50 percent of observations. From this analysis, 266 probesets    were found to be significant predictors of relapse at P<0.01.-   Table 1B lists the accession numbers for Pkey's lacking UnigenelD's    for table 1A. For each probeset is listed the gene cluster number    from which oligonucleotides were designed. Gene clusters were    compiled using sequences derived from Genbank ESTs and mRNAs. These    sequences were clustered based on sequence similarity using    Clustering and Alignment Tools (DoubleTwist, Oakland Calif.).    Genbank accession numbers for sequences comprising each cluster are    listed in the “Accession” column.

Table 1C shows genomic positioning for those Pkey's lacking Unigene ID'sand accession numbers in table 1A. For each predicted exon, is listedthe genomic sequence source used for prediction. Nucleotide locations ofeach predicted exon are also listed. TABLE 1A Pkey ExAccn UnigeneIDUnigene Title p value 428664 AK001666 Hs.189095 similar to SALL1 (sal(Drosophila)-like 3.80177E−05 439785 AA845608 Hs.132860 ESTs 0.000106034413924 AL119964 Hs.75616 seladin-1 0.000157824 459680 H96982 Hs.42321ESTs 0.00019382 431542 H63010 Hs.5740 ESTs 0.000250668 404824C22000161*: gi|2443331|dbj|BAA22375.1|(D 0.000290214 446021 BE389213Hs.286 ribosomal protein L4 0.000320882 434999 AW975059 gb: EST387164MAGE resequences, MAGN Homo 0.000341555 458509 AA654650 Hs.282906 ESTs0.000351184 406722 H27498 Hs.293441 Homo sapiens SNC73 protein (SNC73)mRNA, 0.000536315 423381 BE250014 gb: 600943007F1 NIH_MGC_15 Homosapiens c 0.000602528 419037 R39895 Hs.257391 hypothetical proteinDKFZp761J1523 0.00065526 414898 AA157726 Hs.264330 N-acylsphingosineamidohydrolase (acid c 0.000707085 404582 Target Exon 0.00074185 458607AV656002 ESTs, Moderately similar to unnamed prot 0.000805762 402861D14661 Wilms' tumor 1-associating protein 0.000870602 441494 AW452344Hs.129977 ESTs 0.000875883 452753 AA028049 Hs.277728 SEC14 (S.cerevisiae)-like 2 0.000934337 422516 BE258862 Hs.117950 multifunctionalpolypeptide similar to S 0.000969694 443675 AI081397 ESTs 0.000984435425297 AA354685 gb: EST63062 Jurkat T-cells V Homo sapien 0.001036315419517 AF052107 Hs.90797 Homo sapiens clone 23620 mRNA sequence0.001065289 441345 AW068579 Hs.7780 Homo sapiens mRNA; cDNA DKFZp564A072(fr 0.00111943 438611 AW204707 Hs.123387 ESTs 0.001135255 434949AW976087 ESTs, Highly similar to AF161437 1 HSPC3 0.001142057 430845AF024690 Hs.248056 G protein-coupled receptor 43 0.001172874 429446AI547111 gb: PN2.1_A01_G12.r mynorm Homo sapiens c 0.001185816 444773BE156256 Hs.11923 hypothetical protein 0.001200592 446702 R44518Hs.143496 ESTs 0.001311934 415179 D80630 gb: HUM091D02B Human fetalbrain (TFujiwa 0.0013887 448479 H96115 Hs.21293 UDP-N-acteylglucosaminepyrophosphorylas 0.001402576 430799 C19035 Hs.164259 ESTs 0.001404901454930 AW845987 Hs.68864 ESTs, Weakly similar to phosphatidylseri0.001417466 407241 M34516 gb: Human omega light chain protein 14.10.001504145 421970 AF227156 Hs.110103 RNA polymerase I transcriptionfactor RR 0.001519398 434808 AF155108 Hs.256150 Homo sapiens , Similarto RIKEN cDNA 2810 0.001610938 400207 Eos Control 0.00161581 423318AW467064 Hs.5740 ESTs 0.001622161 413102 AI199981 Hs.109694 ESTs, Weaklysimilar to T27691 hypotheti 0.001683835 411630 U42349 Hs.71119 Putativeprostate cancer tumor suppresso 0.001688301 419872 AI422951 Hs.146162ESTs 0.001710345 402812 NM_004930*: Homo sapiens capping protein0.001742994 427418 AA402587 LAT1-3TM protein 0.001743363 416276 U41060Hs.79136 LIV-1 protein, estrogen regulated 0.001830512 457397 AW969025Hs.109154 ESTs 0.001994494 403372 AW249152 sirtuin (silent mating typeinformation 0.002012497 415344 T65456 gb: yc73a11.r1 Soares infant brain1NIB H 0.002025172 422017 NM003877 Hs.110776 STAT induced STATinhibitor-2 0.002053043 406554 Target Exon 0.002105231 446057 AI420227Hs.149358 ESTs, Weakly similar to A46010 X-linked 0.002151173 407040X03689 gb: Human mRNA fragment for elongation fa 0.002199926 419657AK001043 Hs.92033 integrin-linked kinase-associated serine 0.002290654457662 AA907734 Hs.124895 ESTs 0.002413693 447308 AI005334 Hs.22015ESTs, Weakly similar to 138344 titin, ca 0.002472822 420707 BE312807Hs.143407 ESTs, Weakly similar to A54849 collagen 0.002479439 426429X73114 Hs.169849 myosin-binding protein C, slow-type 0.00251185 429289AI400746 Hs.62187 phosphatidylinositol glycan, class K 0.002513019454275 AW293900 Hs.304842 ESTs, Weakly similar to AMYH_YEAST GLUCO0.002559888 408603 R25283 Hs.326416 Homo sapiens mRNA; cDNADKFZp564H1916 (f 0.002571063 434614 AI249502 Hs.29669 ESTs 0.002629652406558 C5000893: gi|6226859|sp|P38525|EFG_THEMA 0.002723963 440325NM003812 Hs.7164 a disintegrin and metalloproteinase doma 0.002768837440518 AA888046 Hs.233235 ESTs 0.002805131 424099 AF071202 Hs.139336ATP-binding cassette, sub-family C (CFTR 0.002848507 421655 AA464812 gb:zw63h05.r1 Soares_total_fetus_Nb2HF8_(—) 0.002855486 445375 AW779857Hs.166987 ESTs, Weakly similar to B35363 synapsin 0.002861874 456647AI252640 Hs.110364 peptidylprolyl isomerase C (cyclophilin 0.002867794433293 AF007835 Hs.32417 hypothetical protein MGC4309 0.002897453 430389AL117429 Hs.240845 DKFZP434D146 protein 0.002920262 423479 NM014326Hs.129208 death-associated protein kinase 2 0.00294831 443884 N20617Hs.194397 leptin receptor 0.002997251 457926 AA452378 Homo sapiens mRNA;cDNA DKFZp547J125 (fr 0.003054911 459710 AI701596 Hs.121592 ESTs0.003061123 404560 Target Exon 0.003092402 438657 AI141396 Hs.158741ESTs 0.003131957 400282 NM_005313: Homo sapiens glucose regulated0.003134356 416144 AA381556 Hs.331803 heat shock 60 kD protein 1(chaperonin) 0.003162736 430677 Z26317 desmoglein 2 0.003170664 423562AJ005197 Hs.7984 pleckstrin homology, Sec7 and coiled/coi 0.003217503401040 C11000425: gi|4507721|ref|NP_003310.1|ti 0.003244184 419733AW362955 Homo sapiens cDNA FLJ14415 fis, clone HE 0.003251143 415439R21114 Hs.21383 ESTs 0.003317352 458054 AW979052 Hs.5734 meningiomaexpressed antigen 5 (hyaluron 0.003355436 435346 AI248389 Hs.188105 ESTs0.00337758 410452 AW749026 gb: RC3-BT03 19-100100-012-b05 BT0319 Homo0.003407284 427548 AA813784 Hs.123001 ESTs 0.003456322 438918 AI126484Hs.127486 ESTs 0.00347913 448076 AJ133123 Hs.20196 adenylate cyclase 90.003583335 420339 AW968259 Hs.186647 ESTs 0.003607275 426514 BE616633Hs.170195 bone morphogenetic protein 7 (osteogenic 0.003628615 452143N29649 Hs.260855 Homo sapiens cDNA: FLJ21410 fis, clone C 0.003701377422813 AV656571 Hs.121068 transmembrane 4 superfamily member 60.00379349 401524 Target Exon 0.003793904 453768 BE382670 Hs.198511 Homosapiens mRNA; cDNA DKFZp7611177 (fr 0.003810346 424954 NM000546 Hs.1846tumor protein p53 (Li-Fraumeni syndrome) 0.003826169 440409 AW294316Hs.125608 ESTs 0.003879241 452286 AI358570 Hs.123933 ESTs, Weaklysimilar to ZN91_HUMAN ZINC 0.003898535 444756 AA278501 Hs.200332hypothetical protein FLJ20651 0.003922529 429769 NM004917 Hs.218366kallikrein 4 (prostase, enamel matrix, p 0.003947007 443403 R01027Hs.133560 ESTs 0.003959306 400219 Eos Control 0.003966793 448489AI523875 gb: tg97d04.x1 NCI_CGAP_CLL1 Homo sapiens 0.004120703 428378AA427571 Hs.98531 ESTs 0.004121896 449909 AA004681 Hs.59432 ESTs0.004158168 425127 AW841272 Hs.330418 Homo sapiens cDNA: FLJ22459 fis,clone H 0.004166839 427485 AF039652 Hs.178655 ribonuclease H10.004198226 416305 AU076628 Hs.79187 coxsackie virus and adenovirusreceptor 0.004214942 415075 L27479 Hs.77889 Friedreich ataxia regiongene X123 0.00422178 414091 T83742 Hs.334616 gb: yd67g02.s1 Soares fetalliver spleen 0.004236934 446415 T27097 Hs.22790 ESTs 0.004250994 407218AA095473 Hs.28505 ubiquitin-conjugating enzyme E2H (homolo 0.004267222436626 W35362 Hs.103012 ESTs 0.00432651 448519 AW175665 Hs.278695 Homosapiens prostein mRNA, complete cds 0.004332167 409841 AW502139 gb:UI-HF-BR0p-ajr-e-05-0-UI.r1 NIH_MGC_5 0.004357117 423022 AA320525Hs.201076 ESTs 0.004401104 429332 AF030403 Hs.199263 Ste-20 relatedkinase 0.004405129 417834 BE172058 Hs.82689 tumor rejection antigen(gp96) 1 0.004424022 419808 AW008030 Hs.337536 Homo sapiens cDNA:FLJ21568 fis, clone C 0.004471786 450088 AW292933 Hs.254110 ESTs0.004491465 431151 BE207083 gb: ba10d10.y1 NIH_MGC_7 Homo sapiens cDN0.00450798 431281 AW970573 gb: EST382654 MAGE resequences, MAGK Homo0.004657684 420960 Z45662 Hs.90797 Homo sapiens clone 23620 mRNAsequence 0.004798622 409540 AW409569 Hs.101550 gb: fh01e09.x1 NIH_MGC_17Homo sapiens cD 0.004819322 456643 AW751497 Hs.98370 cytochrome P450,subfamily IIS, polypept 0.004821217 449889 AA004613 Hs.168672 ESTs0.004888264 413074 AI871368 Hs.8417 hypothetical protein DKFZp761M04230.004890295 452099 BE612992 Hs.27931 hypothetical protein FLJ10607similar to 0.004925393 434263 N34895 Hs.44648 ESTs 0.004967084 400296AA305627 Hs.139336 ATP-binding cassette, sub-family C (CFTR 0.004996569435981 H74319 Hs.188620 ESTs 0.005005242 409430 R21945 Hs.346735splicing factor, arginine/serine-rich 5 0.005047202 414916 AA206991high-mobility group (nonhistone chromoso 0.005130846 434855 AA765019Hs.191850 ESTs 0.005199586 406651 AI559224 gb: tq32c02.x1 NCI_CGAP_Ut1Homo sapiens 0.005212356 440675 AW005054 Hs.47883 ESTs, Weakly similarto KCC1_HUMAN CALCI 0.005249269 437412 BE069288 Hs.34744 Homo sapiensmRNA; cDNA DKFZp547C136 (fr 0.005270232 400487 ENSP00000238977*:Interferon-induced prot 0.005353963 443366 AI053501 Hs.278869 ESTs,Moderately similar to 2109260A B c 0.005371997 410054 AL120050 Hs.58220Homo sapiens cDNA: FLJ23005 fis, clone L 0.005404329 409344 R47279Hs.285673 hypothetical protein FLJ20950 0.005429984 421215 AI868634Hs.246358 ESTs, Weakly similar to T32250 hypotheti 0.005442884 450661AW952160 ESTs 0.005447857 424269 AW137691 Hs.104696 ESTs 0.005483308412294 AA689219 poly(A)-binding protein, nuclear 1 0.005530138 404511NM_004349: Homo sapiens core-binding fact 0.005558982 437006 AW976322Hs.291561 ESTs 0.005639929 432989 NM014074 PRO0529 protein 0.00572161417584 AA252468 Hs.1098 DKFZp434J1813 protein 0.005734515 437992AW450086 Hs.145989 ESTs, Highly similar to DHHC-domain-cont 0.005769051447506 R78778 Hs.29808 Homo sapiens cDNA: FLJ21122 fis, clone C0.005799441 420929 AI694143 Hs.296251 programmed cell death 4 0.00585145415121 D60971 Hs.34955 Homo sapiens cDNA FLJ13485 fis, clone PL0.005963023 404662 Target Exon 0.006001874 445878 AI262974 Hs.145587ESTs 0.006055258 421090 BE301870 Hs.101813 solute carrier family 9(sodium/hydrogen 0.006079413 405155 Target Exon 0.006110052 427379D79254 Hs.256066 ESTs 0.006133565 412561 NM002286 Hs.74011lymphocyte-activation gene 3 0.006142277 434257 AF121255 Hs.193053eukaryotic translation initiation factor 0.006144213 400141 Eos Control0.006200101 453359 AA448787 Hs.24872 ESTs 0.006315475 433151 AW973735Hs.17631 hypothetical protein DKFZp434E2135 0.006324267 449791 AI248740Hs.133323 ESTs 0.006355539 405722 BE410124 NM_021120: Homo sapiensdiscs, large (Dro 0.006388997 427527 AI809057 Hs.293441 immunoglobulinheavy constant mu 0.006397862 411487 AF116666 Hs.70333 hypotheticalprotein MGC10753 0.006474544 417407 AA923278 Hs.290905 ESTs, Weaklysimilar to protease [H. sapi 0.00651405 437233 D81448 Hs.339352 Homosapiens brother of CDO (BOC) mRNA, 0.006535001 443425 AI056776 Hs.133397ESTs, Weakly similar to 178885 serine/th 0.006574089 409179 BE062633Hs.28338 KIAA1546 protein 0.006647277 431947 AL359613 Hs.49933hypothetical protein DKFZp762D1011 0.006663987 402339 NM_003425*: Homosapiens zinc finger prot 0.006744987 422262 AL022315 Hs.113987 lectin,galactoside-binding, soluble, 2 0.006803463 404458 CX000877*:gi|11877268|emb|CAC18893.1| (A 0.006816499 431693 AI459519 serine (orcysteine) proteinase inhibito 0.006849491 428734 BE303044 Hs.192023eukaryotic translation initiation factor 0.00696046 444204 AI129194Hs.143040 ESTs 0.007032748 406837 R70292 Hs.156110 immunoglobulin kappaconstant 0.007051544 442482 NM014039 Hs.8360 PTD012 protein 0.007051611412006 AW451618 ESTs 0.00705506 435354 AA678267 Hs.117115 ESTs0.007095576 403505 M97639 receptor tyrosine kinase-like orphan rec0.007139282 451946 AI824901 Hs.281012 ESTs, Highly similar to stronghomology 0.007271734 433339 AF019226 Hs.8036 glioblastoma overexpressed0.007286776 436924 AA741001 Hs.326006 ESTs 0.007312314 431578 AB037759Hs.261587 GCN2 eIF2alpha kinase 0.007346563 419551 AW582256 Hs.91011anterior gradient 2 (Xenepus laevis) hom 0.007352833 434256 AI378817Hs.191847 ESTs 0.00736484 439778 AL109729 Hs.99364 putativetransmembrane protein 0.0073683 423443 AI432601 Hs.168812 Homo sapienscDNA FLJ14132 fis, clone MA 0.007425186 405293 Target Exon 0.007457507426357 AW753757 Hs.12396 gb: RC3-CT0283-271099-021-a08 CT0283 Homo0.007488395 422921 BE062045 Homo sapiens cDNA: FLJ23260 fis, clone C0.007499187 417501 AL041219 Hs.82222 sema domain, immunoglobulin domain(Ig), 0.007512156 426091 BE544541 Hs.249495 heterogeneous nuclearribonucleoprotein 0.007576069 416974 AF010233 Hs.80667 RALBP1 associatedEps domain containing 0.007594318 449787 AA005341 Hs.283559 ESTs0.007675199 412162 AA100600 Hs.69192 gb: zn63b10.s1 Stratagene HeLa cells3 93 0.007681586 413522 BE145897 gb: MR0-HT0208-221299-204-b07 HT0208Homo 0.007824405 426788 U66615 Hs.172280 SWI/SNF related, matrixassociated, acti 0.007843962 414586 AA306160 Hs.76506 lymphocytecytosolic protein 1 (L-plasti 0.007931767 450382 AA397658 Hs.60257 Homosapiens cDNA FLJ13598 fis, clone PL 0.007975007 404242ENSP00000252213*:SODIUM BICARBONATE COTR 0.008032744 400206 Eos Control0.008161865 441011 AW137447 Hs.126408 ESTs 0.008169197 449223 AB002348Hs.23263 KIAA0350 protein 0.008169995 451776 W45679 Hs.169854hypothetical protein SP192 0.008174536 418354 BE386973 Hs.84229 splicingfactor, arginine/serine-rich 8 0.00821493 435188 AA669512 Hs.116679ESTs, Weakly similar to A42826 T-cell le 0.00826337 415457 AW081710Hs.7369 ESTs, Weakly similar to ALU1_HUMAN ALU S 0.008283276 432981NM002733 Hs.3136 protein kinase, AMP-activated, gamma 1 n 0.008309431433468 AA832055 Hs.170222 ESTs, Weakly similar to ALU1_HUMAN ALU S0.008310151 457269 AI338993 Hs.134535 ESTs 0.00834154 431676 AI685464gb: tt88f04.x1 NCI_CGAP_Pr28 Homo sapiens 0.008414644 426501 AW043782Hs.293616 ESTs 0.008416828 447623 AA350235 Hs.6127 Homo sapiens cDNA:FLJ23020 fis, clone L 0.008419744 429678 N70394 Hs.238956 ESTs0.008452349 444370 NM015344 Hs.11000 leptin receptor overlappingtranscript-1 0.00847352 404557 C8001174*:gi|10432400|emb|CAC10290.1|(A0.008502518 422867 L32137 Hs.1584 cartilage oligomeric matrix protein(pse 0.008537039 441283 AA927670 Hs.131704 ESTs 0.008562466 424640AA344559 Hs.164428 ESTs 0.008568818 452793 AW138760 Hs.61484 ESTs0.008570907 420527 AA332287 Hs.175110 ESTs 0.00858412 421515 Y11339Hs.105352 GalNAc alpha-2, 6-sialyltransferase I, 1 0.008588847 430316NM000875 Hs.239176 insulin-like growth factor 1 receptor 0.008606329436524 AA922236 Hs.221037 ESTs 0.008616325 444700 NM003645 Hs.11729fatty-acid-Coenzyme A ligase, very long- 0.008668985 441222 AI277237Hs.44208 hypothetical protein FLJ23153 0.008703638 429170 NM001394Hs.2359 dual specificity phosphatase 4 0.008704913 454393 BE153288 gb:PM0-HT0335-180400-008-c08 HT0335 Homo 0.008716471 456107 AA160000Hs.137396 ESTs, Weakly similar to JC5238 galactosy 0.008767147 402091Target Exon 0.008853214 409115 AI223335 Hs.50651 Janus kinase 1 (aprotein tyrosine kinas 0.008866852 423250 BE061916 Hs.125849 chromosome8 open reading frame 2 0.008901601 428944 AA780181 Hs.41182 Homo sapiensDC47 mRNA, complete cds 0.008970935 419052 T83291 Hs.220624 ESTs0.008998014 446203 Z47553 Hs.14286 flavin containing monooxygenase 50.009023814 428180 AI129767 Hs.182874 guanine nucleotide binding protein(G pr 0.009035339 452264 AU077013 Hs.28757 transmembrane 9 superfamilymember 2 0.009036494 446425 AW295364 Hs.255418 ESTs 0.009058296 446547AI334965 Hs.176976 ESTs 0.009087495 419555 AA244416 gb: nc07d11.s1NCI_CGAP_Pr1 Homo sapiens 0.009114049 422068 AI807519 Hs.104520 Homosapiens cDNA FLJ13694 fis, clone PL 0.009119167 434826 AF155661 Hs.22265pyruvate dehydrogenase phosphatase 0.009188183 411950 T28407 Hs.81564platelet factor 4 0.009188186 457146 BE271371 biphenyl hydrolase-like(serine hydrolas 0.009228646 454131 AI215902 Hs.88845 ESTs, Highlysimilar to T50835 hypotheti 0.009282618 404483C8000657*:gi|1504040|dbj|BAA13219.1|(D8 0.009290064 421351 AU076667Hs.103755 receptor-interacting serine-threonine ki 0.00929738 417963AA210718 Hs.104157 ESTs, Weakly similar to KIAA0694 protein 0.009334158429011 AA443182 Hs.188835 ESTs 0.009370261 425380 AA356389 Hs.32148AD-015 protein 0.009402223 442315 AA173992 Hs.7956 ESTs, Moderatelysimilar to ZN91_HUMAN Z 0.009446269 424546 BE465173 Hs.194031 ESTs0.009446339 444524 AI160643 Hs.28332 Homo sapiens cDNA: FLJ21560 fis,clone C 0.009472535 408446 AW450669 Hs.45068 hypothetical proteinDKFZp4341143 0.009508794 422669 H12402 Hs.119122 ribosomal protein L13a0.00950994 420593 AA280356 Hs.187634 ESTs 0.009517511 447502 AA312531Hs.26471 Bardet-Biedl syndrome 4 0.0096083 412825 AW167439 Hs.190651Homo sapiens cDNA FLJ13625 fis, clone PL 0.009645426 434401 AI864131Hs.71119 Putative prostate cancer tumor suppresso 0.009778291 432826X75363 Hs.250770 ACO for serine protease homologue 0.009849589 428840M15990 Hs.194148 v-yes-1 Yamaguchi sarcoma viral oncogene 0.009881804413592 AA130654 Hs.302274 Homo sapiens cDNA FLJ12328 fis, clone MA0.009899125 443102 AI247472 Hs.132965 ESTs 0.009964996Pkey: Unique Eos probeset identifier numberExAccn: Exemplar Accession number, Genbank accession numberUnigeneID: Unigene numberUnigene Title: Unigene gene titlep value: p value for relapse prediction (see Table 1A description)

TABLE 1B Pkey CAT Number Accession 409841 1156088_1 AW502139 AW502432AW502235 AW501683 AW502647 410452 1204142_1 AW749026 BE066111 T97135412006 127108_1 AW451618 AA846096 AI004201 AI242026 N38791 AI032976AA099469 N45423 412294 128797_2 AA689219 AI983045 T16928 Z45040 R20321413522 1374614_1 BE145897 BE145816 BE145885 414916 15071_24 AA206991BE564126 AA092392 AA090034 AA090545 AA093840 N84434 BE269369 AI535705AI535744 AI535682 AF283771 H28296 H27400 BE618821 AI873907 BE622711AI471738 AA557452 AA304303 AW794938 AA600212 AW027283 AW938645 AI654646AA370554 AA356536 AA715713 N87841 AW575412 AA987424 AA319424 BE084055AA827973 AA330422 AW630429 N38949 AA360952 AA045606 BE257213 AW768545AA101746 AI335554 N26696 AI630155 AW170282 AA206705 AA357094 AW603120AW793181 AI127978 AA639183 AW020136 BE536372 AA093946 AA730118 BE079411T90564 D83849 D20752 W07682 BE540914 F22618 AI041775 AA196344 AA366696AA083771 AA054783 AA330028 BE544267 AA247271 AW958331 BE073175 AW945457AA229491 AW874401 R34185 R81133 W32781 AI191194 BE277231 W79255 AW800102AI935842 AA928301 AA230310 AI742195 BE566990 AW673140 AI829489 AA054719AW512749 AA782987 AI088142 AW103898 AA714697 AW574795 AI056134 AW162373BE148890 AW068721 AW076120 AA563764 AW016252 AW016253 AI338171 AI085967AI338788 BE542084 AI186025 AI963188 AW079946 AI034040 AI961313 AI831345N79755 AA029435 AA910600 AA618386 AI336429 AA230308 AI346567 AA541647AW024986 AI926174 AA878167 AW026237 AA668251 W15170 AA129635 AI363729AA309687 AI453176 AI282417 H89557 AW264978 D55190 AA188911 AI471512AI537126 AW675575 AI673287 AI476121 AA563901 AA353344 N93269 N80559L13805 AA564621 AI056119 AI587020 AW874624 AI803890 AW074286 AA745955AW152331 AI282228 AI081139 AI147252 AI126996 AI970694 D55874 AA313759AW023735 AA999920 AI285652 AI476553 AI252804 AI096960 AW151090 AA876366W32423 D57151 AA856637 AI954376 W73923 AL047978 BE041344 AA861867AI346526 AL047979 AI348036 AI187244 AA328683 AA197248 N72984 AA862752AA747207 AA876587 AA845496 AA890470 AW170401 AI127224 N99881 AW074379AA938114 AI197777 AI753834 AI346536 AA331597 AI367738 AA977063 W93785AA872167 AI932924 AA614560 AI434283 AI160153 AW130136 BE542026 AA385117AA130703 AA778269 AI769329 AI285034 AW340835 AI224601 AA663430 AA846183AI362627 AA903448 AW238760 AI283178 AV662138 AI138363 AA860743 AI368179AI280190 AI139131 AI359157 H99812 AA771749 AI539068 AI089843 AI566789AI281240 AI352354 AI769243 AI092187 AI073627 AI473623 AW276039 AI798397AI024587 AA889467 AI683918 AW673268 AA602941 AA861823 AA668586 AA772542AI077928 AA594116 AI018648 AI421799 AA705955 AA586855 AA577106 AI131297AI355412 AI350882 AW265014 AW043934 AI127696 AW469864 AI041801 AL048264AA961777 AI246050 AA566002 AI469308 AA809086 AW768947 AA507781 AI361342AI368477 AA133897 AI300444 AI768467 AA773978 AW062352 AA648130 AA827606AI094950 T61248 AA101747 AI348251 AI092294 AA565522 T39158 W33201 C75489AA670425 AA483085 R48684 T28966 H96803 AA641999 AA709360 H99805 T19371AW879059 AA524370 AW338262 N72895 AW591714 T63777 AL047945 AA150131AA146973 AW878989 AA877803 T56122 AA147065 AA342484 AA342236 AW270920AI913364 AW795486 AI865002 W94286 AA209325 T40443 AI268918 AI418552T48135 M62207 AA328164 AW795480 BE169953 BE169983 AA206888 AA132394AW149866 T57929 W15510 C75674 R81132 AI423687 AI193465 H28297 AA994473F04357 BE243460 AA987347 AI376779 AA927274 T03381 H99134 T03851 AA384714AW265058 BE041328 BE541757 AI910675 T64485 N89843 AA688338 T64628AI143530 AI026855 F03043 AA865434 AA363018 AA459233 AA664746 N68567AW467363 T16030 AW149914 AA994312 BE350136 AA307427 AI658528 L13804AA384004 N71219 N22172 AW364964 415179 1527481_1 D80630 D80896 D80895415344 1534510_1 T65456 F11749 Z43023 F06216 R18181 R17246 419555185884_1 AA244416 AA244401 419733 187589_1 AW362955 H59488 AI040666W60959 W94209 H27231 T84625 H75715 W04957 W63676 AA659693 AA514302W63789 BE046412 T91396 AI951970 AW044233 N20018 AW663548 T90114 AI139947AA809643 AA846232 AA581966 AA789002 AA295134 AW188870 H75644 AA526037AA347970 AW961788 H61476 AL133779 AA449282 H28581 AA249370 421655204993_1 AA464812 AA431899 AA295193 AW959241 422921 222939_1 BE062045Z43804 W35143 AI761615 N33753 BE062044 BE551229 AI088004 N33865 AA332473AA374196 N48481 423381 227731_1 BE250014 BE293608 BE252781 AA325222AW904396 425297 249704_1 AA354685 AW962101 H85269 F11427 R55281 427418278594_1 AA402587 AI760178 AI911270 AI184927 AI277654 AA402398 AI633280AW002589 AI984968 AI810234 AI671725 AI419580 AA705629 AW138044 AI719961D45607 AA455831 429446 304683_1 AI547111 AW973749 AA558007 430677 3216_1Z26317 NM_001943 AW991316 BE018413 AW996800 AW996267 AW996264 W73983AA313797 BE513193 AW861416 AW857494 AA488331 BE171045 AW366926 BE002219AW996792 AW753487 AW361908 BE003946 AW858751 AW858747 AW858750 AW858755AW858749 D58979 AW363740 AW859003 AW363742 AW858999 AW471344 BE072891AW753745 BE395396 AI378517 D58730 AW748942 BE395765 BE153312 BE153169BE153241 AW371849 AW371853 AW748956 AA506621 AA723159 AI933746 AW473996AW572140 431151 328652_−1 BE207083 431281 330904_1 AW970573 AA501880AA501870 431676 336411_1 AI685464 AW971336 AA513587 AA525142 43169333663_3 AI459519 AI366092 AF121175 AL042956 F11899 AI436382 AI133591AI675879 AA081306 AI948730 BE243645 AA448957 H09862 AI382265 N92723AL048959 AI356415 BE245782 AI288626 AI949306 AI814412 AW207026 AI659678AI984766 AA741391 AI453490 AW166423 AI799883 AL045697 AI826075 AI952039AA167742 BE463776 R01203 AI972947 AI623819 AW167132 AW337996 AW264027AA209462 AI863491 T65400 AI394192 R62397 AW968250 BE464852 AW474624AI758979 AW474705 BE046016 AI949348 AI289432 AI620722 AW440580 AI610824AI458169 AW002172 AI634183 AA648408 AI289435 C00469 R62398 AI287482H24845 F09546 AI125609 W93405 AA150039 AA150203 H09775 AI951377 AI631154AA258738 AA744971 AA449685 AI434048 AA167836 R01316 T54772 43298935719_1 NM_014074 AF111848 434949 39603_1 AW976087 AA100561 AF161437D30850 AA767385 AI990080 AI337209 AA086348 AW002909 AA747908 AW450816AW361653 BE145974 BE146300 AW292658 434999 397353_1 AW975059 AA659177AA733194 443675 577019_1 AI081397 N94610 AI633993 AW949183 W23817AW297357 H17610 F32559 448489 765247_1 AI523875 R45782 R45781 45066184193_1 AW952160 AI819147 AA774089 AA010589 AA319638 AI954753 AI634083H39119 AA812766 454393 115888_1 BE153288 BE153151 BE152925 AA078302457146 29193_1 BE271371 NM_004332 X81372 AI167945 AW071802 AI818871AI017491 AA421820 AA558952 AA910750 AA973795 R54850 AI672895 AI418120AI268326 AA911487 AA167197 N46097 X57653 R10551 T28159 AA167111 AW840204AW276222 R09405 N46098 AA284554 AW129121 457926 43767_1 AA452378AL390181 H05571 R53363 R55079 R11987 R11919 R84811 R19546 AA046904R22842 AL134431 F11225 W79925 H10691 AA354088 AW965695 AI198775 AI803682AA040404 AI150653 AA040266 AI436656 AW575893 AI703024 AA446858 AI805847AI699312 AW575924 R55051 R53965 R39826 AW772031 AA975258 AW901905 R43388BE218163 AI074604 AI148281 AA758256 BE501159 H11032 AW131553 F08888AW341569 AI347996 AI952708 AI374835 AI089094 AI284927 W74206 AI027303AI274177 AW299757 AI377712 AW300882 AA883979 AI239912 AI346165 AA947211R46050 AI698833 AA452150 R43898 AA904733 458607 65602_1 AV656002AV655810Pkey: Unique Eos probeset identifier numberCAT number: Gene cluster numberAccession: Genbank accession numbers

TABLE 1C Pkey Ref Strand Nt_position 400487 8919452 Plus 19369-20782401040 7232177 Plus 17623-17919 401524 7770429 Plus 34644-35263 4020918117554 Minus 190-306 402339 7459859 Minus 24698-26511 402812 6010110Plus 25026-25091, 25844-25920 402861 2814366 Minus 14933-15231,15387-15627 403372 9087278 Minus 130002-130131 403505 7577651 Plus11059-11541 404242 5672600 Minus 22722-22897, 23164-23433 404458 7770571Minus 35710-36276 404483 8096904 Minus 162212-163710 404511 8151864Minus 148501-148659 404557 7243881 Minus 88508-88699 404560 8954219 Plus29247-29437 404582 9739220 Plus 53230-53424 404662 9797105 Minus99466-99713 404824 6478944 Plus 209436-209545, 209741-209850 4051559966228 Plus 130469-130723 405293 3845419 Minus 16255-16535, 16665-17340405722 9800078 Plus 140732-140892, 141099-141268, 141434-141714,142048-142192 406554 7711566 Plus 106956-107121 406558 7711569 Minus14052-14190Note:the ExAccn number of NM_(—) is abbreviated to NM in Table 1A-C.Pkey: Unique number corresponding to an Eos probesetRef: Sequence source. The 7 digit numbers in this column are GenbankIdentifier (GI) numbers. “Dunham I. et al.” refers to the publicationentitled “The DNA sequence of human chromosome 22.” Dunham I. et al.,Nature (1999) 402: 489-495.Strand: Indicates DNA strand from which exons were predicted.Nt_position: Indicates nucleotide positions of predicted exons.

Table 2 lists the first 50 genes, ranked by P value, identified bysurvival analysis to be associated with prostate cancer relapse. TABLE 2Rank UniGene cluster Genbank accession Gene title Risk of relapse^(a) P1 Hs.189095 NM_020436 Sal-like 4 2.040 0 2 Hs.132860 AA845608 ESTs 0.3410 3 Hs.75616 NM_014762 24-Dehydrocholesterol reductase (seladin-1) 0.2930 4 Hs.42321 NM_173605 Hypothetical protein LOC283518 2.133 0 5 Hs.80667NM_004726 RALBP1 associated Eps domain containing 2 (REPS2) 0.172 0 6Hs.163543 NM_144704 Hypothetical protein FLJ30473 3.241 0 7 Hs.286NM_000968 Ribosomal protein L4 0.215 0 8 Hs.114670 D49387 Leukotriene B412-hydroxydehydrogenase 2.380 0 9 Hs.366053 NM_024080 Transient receptorpotential cation 0.260 0 channel, subfamily M, member 8 (trp-p8) 10Hs.366 AL389978 Immunoglobulin heavy chain variable region 2.436 0.00111 Not available BE250014 ESTs 0.295 0.001 12 Hs.257391 NM_032293Hypothetical protein DKFZp761J1523 3.138 0.001 13 Hs.264330 AK024677N-acylsphingosine amidohydrolase (acid ceramidase)-like 0.256 0.001 14Hs.123468 NM_033225 CUB and Sushi multiple domains 1 0.185 0.001 15 Notavailable AV656002 EST 0.251 0.001 16 Hs.129977 AW452344 ESTs 0.2290.001 17 Hs.277728 NM_012429 SEC14-like 2 0.348 0.001 18 Hs.117950NM_006452 Phosphoribosylaminoimidazole carboxylase 0.321 0.001 19Hs.424973 BC018081 Clone IMAGE: 4793702 0.225 0.001 20 Not availableAA354685 EST 0.363 0.001 21 Hs.356547 NM_138799 Hypothetical proteinBC016005 0.337 0.001 22 Hs.7780 AL049969 cDNA DKFZp564AO72 0.186 0.00123 Hs.123387 AW204707 ESTs 0.375 0.001 24 Hs.377879 AK055649 cDNAFLJ31087 fis 3.112 0.001 25 Hs.248056 NM_005306 G protein-coupledreceptor 43 0.211 0.001 26 Hs.301947 NM_014509 Kraken-like serinehydrolase 0.212 0.001 27 Hs.11923 NM_018982 Hypothetical proteinDJ167A19.1 0.155 0.001 28 Hs.247423 NM_001617 Adducin 2 (β) (ADD2) 2.0440.001 29 Not available D80630 EST 2.753 0.001 30 Hs.21293 NM_003115UDP-N-acteylglucosamine pyrophosphorylase 1 0.185 0.001 31 Hs.292859C19035 ESTs, moderately similar to VPP2_HUMAN 2.375 0.001 32 Hs.68864AW845987 Lipase, member H (LIPH), mRNA 0.273 0.001 33 Hs.405944 X57819Ig λ chain 2.388 0.002 34 Hs.110103 NM_018427 RNA polymerase Itranscription factor RRN3 0.337 0.002 35 Hs.256150 NM_080654 NY-REN-41antigen 2.718 0.002 36 Hs.76847 NM_014610 α Glucosidase II alpha subunit0.135 0.002 37 Hs.109694 AI199981 Oxysterol binding protein-like 8(OSBPL8), mRNA 4.511 0.002 38 Hs.71119 NM_006765 Putative prostatecancer tumor suppressor (N33) 0.281 0.002 39 Hs.146162 AK075364 ESTs2.151 0.002 40 Hs.333417 NM_004930 Capping protein (acun filament)muscle Z-line, β 0.291 0.002 41 Hs.410998 AA402587 ESTs, Highly similarto MLL septin-like fusion 1.507 0.002 42 Hs.79136 NM_012319 LIV-1protein, estrogen regulated 0.210 0.002 43 Hs.109154 AW969025 ESTs 0.2810.002 44 Hs.433622 NM_007085 Follistatin-like 1 (FSTL1) 0.233 0.002 45Not available T65456 EST 0.195 0.002 46 Hs.405946 NM_003877 Suppressorof cytokine signaling 2 (SOCS2) 0.448 0.002 47 Hs.127699 NM_001369Dynein, axonemal, heavy polypeptide 5 (DNAH5) 0.284 0.002 48 Hs.422118NM_001402 Eukaryotic translation elongation factor 1 alpha 1 0.175 0.00249 Hs.92033 NM_030768 Integrin-linked kinase-associated 5.564 0.002serine/threonine phosphatase 2C 50 Hs.124895 AA907734 ESTs 3.399 0.002^(a)The risk of relapse is the IQR HR calculated for each probeset asdescribed in “Materials and Methods.”

Sequences described therein, where incomplete, may be extended either byinformatics techniques, or by techniques of biochemistry and molecularbiology. Many well known methods are available. See, e.g., Mount (2001)Bioinformatics: Sequence and Genome Analysis CSH Press, NY; Baxevanisand Oeullette (eds. 1998) Bioinformatics: A Practical Guide to theAnalysis of Genes and Proteins (2d. ed.) Wiley-Liss; Ausubel, et al.(eds. 1999 and supplements) Current Protocols in Molecular BiologyLippincott; and Sambrook, et al. (2001) Molecular Cloning: A LaboratoryManual (3d ed., Vol. 1-3) CSH Press.

Nucleic acid sequences are particularly described. Using linkages topublicly accessible databases, e.g., GenBank accession numbers,sequences are described whose presence or absence in the samplesprovides prognostic capacity. Correlations are made between thedetection of such sequence and the outcomes of the prostate cancers.Thus, detection of physically linked, e.g., adjacent or contiguous,sequence will be equivalent. The correlation between presence of a 5′segment will be equivalent to such with a 3′ segment of the samephysical molecule.

The tables also provide protein sequences which correspond to theidentified nucleic acid sequences. The amino acid embodiments of themarkers will also exhibit similar correlations with outcome. Thus, theuse of the protein embodiments can also be used in the invention.Proteins or fragments can be produced, and antibodies generated. See,e.g., Coligan (1991) Current Protocols in Immunology Lippincott; Harlowand Lane (1988) Antibodies: A Laboratory Manual CSH Press; and Goding(1986) Monoclonal Antibodies: Principles and Practice (2d ed.) AcademicPress.

Kits for use in the prognostic methods are also made available. The kitswill include reagents for detecting the markers, e.g., at the nucleicacid or protein level. Thus, for nucleic acid expression level prognosiskits, typically PCR primers or detectable hybridization probes will beincluded. For protein level prognosis kits, typically antibodies will beused to quantitate or detect the appropriate gene products. Typicallyinstructions will be provided, which may include buffers or instructionsfor proper disposal of the materials.

Diagnostic, Therapeutic Applications

After prostate cancer has been identified, tests are performed to findout if cancer cells have spread within the prostate or to other parts ofthe body. Prostate cancer is typically classified into stages I-IV. Thefollowing tests and procedures may be used in the staging process:radionuclide bone scan, pelvic lymphadenectomy, CT scan, and seminalvesicle biopsy.

The list of targets may have other diagnostic applications besidesoutcome prediction. These identified markers may be valuable in suchstage subsetting, distinct from outcome subsetting. Typically, afterinitial diagnosis, tests are performed to determine if cancer cells havespread within the prostate or to other parts of the body. Evaluation ofthe identified markers, singly or in combinations, may substitute forother tests to assign stage, or add to them for confirmation.Alternatively, the detection of one or more of these markers may be usedas early detection screens for prostate cancer. Preferably, if themarker is soluble or released into a readily accessible body fluid,e.g., serum, semen, or urine, a diagnostic test for detection may allowfor early detection of prostate cancer.

The invention is illustrated further by the following examples that arenot to be construed as limiting the invention in scope to the specificprocedures described in it.

EXAMPLES Example 1 Study Design

Tissue Collection and Preparation of RNA

A cohort of 72 fresh-frozen prostate cancers was collected from patientswith localized prostate cancer treated by radical prostatectomy RP atSt. Vincent's Hospital, Sydney. The primary outcome, disease-specificrelapse, was measured from the date of RP and was defined as a rise inserum PSA above 0.3 ng/ml with subsequent further rises. Followinginking of the external limits of the prostate immediately after removaland prior to formalin-fixation, up to six, 5 mm core biopsies were takenand stored at −80° C. for a later RNA extraction. The proportion ofinvasive cancer in the biopsy sample was then estimated retrospectivelyby either frozen sectioning of the biopsy and hematoxylin and eosinstaining, or by examination of archival formalin-fixed,paraffin-embedded tissue surrounding the biopsy site. Only thosebiopsies that contained ≧75% invasive cancer were used for subsequenttranscript profiling. Only one biopsy per patient was analyzed.

Xenograft Model

The androgen-dependent LuCaP-35 (7) prostate cancer xenograft was grownas subcutaneous tumors in nude male mice. To study theandrogen-withdrawal process, tumor-bearing mice were castrated andmonitored for tumor regression and PSA levels. Tumors were harvestedfrom mice prior to castration, and at various time points (1-100 days)post-castration and were processed for microarray analysis. For dataanalysis and identification of androgen-regulated genes, the sampleswere binned in two groups consisting of days 0-2 and days 5-100post-castration. Genes that showed a significant (P<0.01) difference inthe means of each group were identified by a standard Student's t-Test.

RNA Extraction and Microarray Protocols

Preparation of total RNA from fresh-frozen prostate and xenograft tissuewas performed by extraction with Trizol reagent (Life Technologies Inc.,Gaithersburg, Md.) and was reverse transcribed using a primer containingoligo(dT) and a T7 promoter sequence. The resulting cDNAs were then invitro transcribed in the presence of biotinylated nucleotides(Bio-11-CTP and Bio-16-UTP) using the T7 MEGAscript kit (Ambion, Austin,Tex.).

The biotinylated targets were hybridized to the Eos Hu03, a customizedAffymetrix GENECHIP® (Affymetrix, Santa Clara, Calif.) oligonucleotidearray comprising 59,619 probesets representing 46,000 unique sequencesincluding both known and FGENESH predicted exons that were based on thefirst draft of the human genome. Hybridization signals were visualisedusing phycoerythrin-conjugated streptavidin (Molecular Probes, Eugene,Oreg.). Normalization of the data was performed as follows. Theprobe-level intensity data from each array were fitted to a fixed gammadistribution with a mean of 300 and a shape parameter of 0.81. Thisnormalization procedure removes between chip variation attributable tonon-biological factors. Then for each probeset, a single measure ofaverage intensity was calculated using Tukey's trimean of the intensityof the constituent probes (8). Finally, a correction for nonspecifichybridization was applied, in which the average intensity measure of a“null” probeset consisting of probes with scrambled sequence wassubtracted from all other probesets on the chip.

Statistical Methods

Prior to survival analysis, a screen was applied to the expression datato eliminate probesets without meaningful variation. For each probeset,the ratio of the 90^(th) percentile to the 15^(th) percentile intensitymeasure was required to be at least 2, and the minimum expression levelwas required to be at least 150 average intensity units. Separate Coxproportional hazards analyses with pretreatment PSA concentrationdichotomised at 20 ng/ml and gene expression modeled as a continuousvariable were used to identify gene expression that correlated with PSArecurrence (9). The IQR hazards ratio was computed by multiplying theregression coefficient for each probeset by its own interquartile rangeprior to exponentiation. The positive false discovery rate (pFDR) wascalculated using the method described by Storey and Tibshirani (10).Schoenfeld residuals were used to assess the proportional hazardsassumption for the two probesets for trp-p8 and the assumption was foundto be upheld in both cases.

Variables of clinical relevance were also modeled in univariate analysesfor their ability to predict disease-free survival in the 72 prostatecancers using the Cox proportional hazards model. Trp-p8 mRNA expressionassessed by ISH, was reported as proportions within histological groupsand compared between groups using a Fisher's Exact test.

The expression dataset of 277 selected probesets from 72 samples wasreordered according to cluster analysis in both dimensions (probesetsand samples). In each analysis, the distance metric was the square rootof (1−r), where r is the standard pearson product-moment correlation.The clustering algorithm used was Ward's minimum variance method (11).

In order to evaluate the ability of the 11 genes used by Singh et al.,to accurately predict relapse status in aggregate in our dataset, weentered these eleven probesets into a multivariate Cox regression model,and used variable selection methods to choose a subset of predictors.Three different methods were used (forward selection, backwardelimination, and stepwise selection), all using P=0.15 asinclusion/exclusion criterion). In each case, the final model using 4probesets had a significance level of P=0.0029 by the likelihood ratiotest.

All statistical analyses were performed using SAS (SAS Institute Inc.,Cary, N.C.).

Tissue Microarray and In Situ Hybridization

Tissue microarrays were constructed as described previously (12), andwere comprised of prostate cancer samples from 95 patients that are partof a previously published cohort of patients treated for localizedprostate cancer by RP alone at St. Vincent's Hospital, Sydney (13). Inaddition, 13 prostate cancer specimens were collected from patientstreated for localized prostate cancer by RP who had received at least 3months (range 3-10 months) of preoperative neoadjuvant hormonaltreatment (5 with anti-androgens alone, 6 with a combination of a Gn-RHanalogue and anti-androgens and 2 with a Gn-RH analogue alone). Trp-p8expression in these 13 samples was assessed on conventional tissuesections.

For ISH, a 424-base pair probe for trp-p8 was derived from the 3′ end ofthe trp-p8 gene and transcribed to produce a DIG-labeled riboprobe usingan RNA DIG-labeling kit (Roche, Mannheim, Germany). ISH was performed onthe VENTANA DISCOVERY™ instrument (Ventana Medical Systems, Tucson,Ariz.) using the RIBOMAP™ kit with protease P2 for 2 minutes (VentanaMedical Systems, Tucson, Ariz.) and hybridization for 8 hours at 65° C.Chromogenic detection was achieved with the BLUEMAP™ detection system asdescribed by the manufacturer (Ventana Medical Systems, Tucson, Ariz.).

Example 2 Expression Profiling of Prostate Cancers

In this study, we sought to discover novel biomarkers that might predictfor PSA relapse following radical prostatectomy utilizing outcome-basedstatistical tools to analyze gene expression profiles of 72 prostatecancers. A criteria for selection was the ability to predict recurrencebetter than preoperative serum PSA concentration alone, since PSA is oneof only a handful of markers that provide preoperative prognosticinformation. The 72 prostate tissues were collected at the time ofradical prostatectomy (RP) from patients undergoing treatment forlocalized prostate cancer at St. Vincent's Hospital Campus, Sydney,Australia. At last follow-up (median=28.25 months, range 4.9-90.3months), 17 of the 72 (23.6%) patients had relapsed, of which 14demonstrated a rise in postoperative PSA levels while 3 patients werediagnosed with a rising PSA and local recurrence of disease. Consistentwith published data (5, 6, 13), the significant predictors of prostatecancer relapse in this cohort on univariate analysis were Gleason score(HR=1.88, P=0.027), surgical margins (HR=4.90, P=0.035) and preoperativePSA concentration (HR=4.43, P=0.006) (Table 1). The overall relapse rateof 23.6% and median time to relapse of 14 months in this group of 72patients was similar to that observed in a cohort of 732 patientstreated for localized prostate cancer by RP at the same institutionbetween 1986 and 1999 (13). TABLE 3 Clinicopathological characteristicsof the prostate cancer cohort (n = 72) that were utilized in thesurvival analysis. Variable HR (confidence levels) P Gleason score^(a)1.88 (1.08-3.29) 0.027 Preoperative PSA 4.43 (1.53-12.79) 0.006concentration <20 ng/ml vs. ≧20 ng/ml Seminal vesicle involvement 2.33(0.88-6.14) 0.086 positive vs. negative Surgical margins 4.90(1.12-21.5) 0.035 positive vs. negative^(a)Gleason score was modeled as a continuous variable.

RNA was extracted from a core biopsy taken at the time of RP for each ofthe 72 cases that comprised ≧75% cancer tissue. Biotinylated RNA fromeach sample was then analyzed with a customized GENECHIP® expressionarray, the Eos Hu03 (14). This single GENECHIP® microarray design isrepresentative of greater than 90% of the expressed human genome basedon the first public draft and comprises 59,619 probesets representativeof both known and predicted genes (15). An initial screen was applied tothe microarray probesets to choose genes expressed with reliableintensity and adequate cross-sample variance. This screen reduced theinitial set of 59,619 probesets to a subset of 8,521 probesets forfurther examination.

Example 3 Survival Analysis

Each probeset's intensity value was entered as a continuous explanatoryvariable in a Cox proportional hazards survival analysis predictingrelapse. Pretreatment PSA concentration was also entered as a predictorin each analysis. From this analysis, 264 probesets were found to besignificant predictors of relapse at P<0.01. To assist interpretation,we next calculated the interquartile range hazard ratio (IQR HR) foreach probeset. Because the expression data are treated here ascontinuous covariates, hazards ratios expressed in their natural scaleillustrate only the change in risk of relapse associated with a changeof 1 unit on the expression scale, a change too small to be comprehendedeasily. To put the hazard ratios and associated confidence limits on amore interpretable scale, we present here the hazards ratio associatedwith a change in expression values equivalent to 1 interquartile range(IQR) of the sample data for each probeset. The IQR is simply the 75thpercentile minus the 25th percentile, and thus contains the middle 50percent of observations.

The multiple hypothesis testing problem has been recognized as animportant issue to address in microarray research. The large number oftests that are performed simultaneously on thousands of probesetsgreatly increases the chances of making Type I errors (or false-positivefindings). To assess the effect of multiple hypothesis testing, weadapted a method developed by Storey and Tibshirani (2001) forcalculating the positive false discovery rate (pFDR), an estimate of theproportion of false-positives present in a set of findings (10). Thistechnique was developed explicitly for use with microarray data, forwhich the usual assumption of independence among tests is untenable. Theprocedure can be briefly summarized as follows. First, null data weresimulated by randomly permuting the relapse status of subjects andre-performing the survival analyses. In each simulation, the number ofrelapsers and non-relapsers (17 and 55, respectively) remained constant,but these designations were shuffled and assigned to patients at random.The permutation was performed 500 times, and for each simulation, thenumber of findings at P<0.01 was noted. The mean number of findingsacross the 500 permutations was 85.9. This figure, an estimate of theexpected number of false positives under null conditions, was thendivided by the number of actual findings (n=264) to obtain an estimateof the proportion of false-positive findings. After the application of acorrection factor (10), the final estimate for the PFDR was 23%. Thus,we can expect that approximately 61 of the 277 findings are falsepositives.

Identification of the Candidate Marker Genes

The 277 probesets (Table 1A-1C) identified by survival analysis includedboth known genes and hypothetical genes of unknown function, as well asESTs.

Cluster analysis performed in both dimensions on the 72 RP samples andthese 277 probesets using the Ward's minimum variance procedureidentified two gene expression subgroups (FIG. 1). Sixteen of the 17patients known to have experienced a PSA relapse were clustered in onegene expression group characterized by a relative increase in expressionof 85 genes (cluster 1) and loss of expression of 179 genes (cluster 2;FIG. 1). An additional 22 patients that were disease-free at the time ofcensoring were located in this expression cluster, and may suggest thatthese patients have an increased propensity for relapse in the future.Thirty-two patients who were disease-free at the time of censoringconstituted the second expression group which also included one patientwho had experienced a PSA relapse.

Notably, three of the 277 probesets showing strongest correlation withrelapse in our model were identified as the gene for the putativecalcium channel protein, trp-p8 (16). For all three probesets, loss ofexpression of trp-p8 mRNA was associated with a significantly shortertime to PSA relapse free survival with an IQR HR of 0.26 (0.12-0.54;P<0.001), 0.32 (0.16-0.66, P=0.0022) and 0.27 (0.12-0.66, P=0.0045),respectively, when PSA was included in the analysis. Notably, loss oftrp-p8 remained a significant predictor of PSA relapse when modeledalone or with Gleason score (data not shown). Subsequent analysis showedthat expression of trp-p8 mRNA was primarily restricted to the prostate.Low-level expression was detected in normal liver and no detectableexpression was seen in 32 distinct other normal tissues examined byoligonucleotide microarray analysis (FIG. 2 a). These data confirm thefindings of a recent study that also showed that trp-p8 expression wasprostate-specific (16). Analysis of 23 cancer cell lines showed thattrp-p8 is only expressed at very low levels in the androgen-dependentprostate cancer cell line LnCaP, but not in the androgen independentprostate cancer cell lines, PC-3 and DU-145, consistent with previousdata (16). Since this observation alone is not conclusive evidence thattrp-p8 expression is androgen-regulated, we next utilized theandrogen-dependent LuCaP-35 prostate cancer xenograft model to assesschanges in trp-p8 expression that occur during transition from androgendependence to androgen independence of prostate cancer (7). MaleLuCaP-35 mice were castrated and tumors were harvested at several timepoints (0-100 days) after castration. High levels of trp-p8 expressionwere detected on days 0-2 after castration, but not on days 5-100 postcastration, and correlated significantly with PSA expression in the samemice (Pearson P=0.080; FIG. 2, B and C).

To gain further insight into the putative association of trp-p8 withandrogen regulation, we examined the levels of trp-p8 expression in theprostate tissue of patients who were treated with androgen deprivationtherapy (neoadjuvant hormonal therapy, NHT) prior to RP. In situhybridization (ISH) for trp-8 mRNA was performed on RP specimens from 13patients who had received at least 3 months preoperative NHT and thelevels compared with tissue from 95 patients treated with RP alone (FIG.3). These latter patients formed part of a large RP cohort describedpreviously (13). While trp-p8 mRNA was detected in 80 of 95 (84%)prostate cancers from patients treated with RP alone, those patients whounderwent NHT prior to RP demonstrated significantly less expression oftrp-p8, with only 4 of 13 (31%) samples positive for trp-p8 mRNA(Fisher's Exact test, P<0.001; FIG. 3).

Taken together, these data from cell lines, prostate cancer xenograftsand clinical specimens, combined with the original finding that trp-p8mRNA levels correlated strongly with prostate cancer relapse, stronglysupport the conclusion that trp-p8 expression is androgen-regulated andmay be associated with the transition to androgen-independent disease. Amonoclonal antibody to trp-p8 can be produced that will be used toassess protein expression by immunohistochemistry in an independentcohort of formalin-fixed, paraffin-embedded prostate cancer specimenswith known prostate cancer outcome (13).

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It should be apparent that given the guidance, illustrations andexamples provided herein, various alternate embodiments, modificationsor manipulations of the present invention would be suggested to askilled artisan and these are included within the spirit and purview ofthis application and scope of the expanded claims.

1. A method of determining prognosis of prostate cancer from a patient, comprising the steps of: establishing the threshold value of at least one prognostic gene selected from the group of Table 1A, 1B and 1C, determining the amount of said prognostic gene from a prostate tissue of a patient, comparing the amount of said prognostic gene with the established threshold value of said prognostic gene, and determining the prognostic outcome of the patient.
 2. The method according to claim 1, wherein said prognostic gene is trp-p8.
 3. The method according to claim 1, wherein the amount of said prognostic gene is determined after amplification of said prognostic gene.
 4. The method according to claim 1, further comprising determining prostate-specific antigen.
 5. The method of claim 1, wherein said prognosis is determined after a selected therapeutic treatment.
 6. The method according to claim 1, wherein said prognosis contributes to selection of a therapeutic strategy.
 7. The method according to claim 1, wherein said prognosis contributes to selection of a therapeutic strategy.
 8. A method of determining prognosis of prostate cancer from a patient, comprising the steps of: establishing the threshold value of at least one prognostic gene selected from the group of Table 2, determining the amount of said prognostic gene from a prostate tissue of a patient, comparing the amount of said prognostic gene with the established threshold value of said prognostic gene, and determining the prognostic outcome of the patient. 